Selected Research Results of Joel Norris
Indian Ocean Cloud Trends
The recent Indian Ocean Experiment
observed high concentrations of soot aerosol over the northern Indian Ocean
during January-April when air flows offshore from India. The soot particles
absorbed a substantial amount of solar radiation and caused extra daytime
heating of the atmosphere. The modeling study of Ackerman et al.
(2000) suggested that this heating reduces daytime low-level cloud cover.
Soot aerosol has very likely greatly increased over the past fifty years
due to population growth and development in India. If the Ackerman hypothesis
is valid, this should cause a resulting decrease in low-level daytime
cloud cover. However, the observations indicate low-level cloud cover has
increased between 1952-69 and
1980-96 over regions in both the
northern and southern Indian Ocean (indicated by boxes). Thus, other
processes must compensate soot heating to maintain low-level cloudiness.
For more information see:
Global Climate Model Cloud Diagnostics
Global climate models (GCMs) have difficulty correctly simulating clouds.
Because clouds play a key role in the climate system, it is essential that we
diagnose the source of problems in GCM cloud simulation. The following figures
compare observed and NCAR Community Climate Model version 3 (CCM3) simulated
cloud properties over the North Pacific during July.
Global Ocean Cloud Trends
Synoptic surface observations indicate total cloud cover and
low-level cloud cover have
increased at every latitude over the ocean between 1952 and 1995 (thin lines
are annual mean cloud cover for each 10 degree latitude zone and the thick
dashed line is annual mean cloud cover for the global ocean).
Latitudinal trends are smallest
at Northern Hemisphere middle latitudes (horizonal lines indicate global mean
trends). Some possible causes of increased global ocean cloud cover are:
Summertime North Pacific Variability
The dominant patterns of interannual variability over the North Pacific during
summer for various fields are:
These are calculated by Empirical Orthogonal Function (EOF) analysis
separately for each field. Time series are normalized, and colors indicate
anomaly patterns corresponding to one standard deviation in the time series.
Contours indicate the climatology. The variations correspond to latitudinal
shifts in the locations of the mean storm track and the mean gradients in sea
surface temperature and low stratiform cloud. Not shown is the pattern of sea
level pressure (a weakening of the poleward flank of the mean subtropical
anticyclone). Aside from sea level pressure, the variations are
highly coupled in time.
For more information see:
Midlatitude Boundary Layer Cloud Transitions
Low cloud type climatologies show an abrupt latitudinal
transition between stratiform
and cumuliform cloudiness during summer that is co-located with the
western North Pacific sea
surface temperature (SST) gradient. Examination of the latitudinal
distribution of various cloud types and the frequencies at which they occur in
northerly and southerly flow indicates that advection over the SST gradient
greatly influences boundary layer structure and hence cloud type. Equatorward
advection over the SST gradient promotes a
transition from stratocumulus to
cumulus under stratocumulus to cumulus. Poleward advection over the SST
gradient promotes a transition from
cumulus to cloudless boundary layer to shallow stratus.
For more information see:
Low Cloud Type Climatologies
Synoptic surface cloud type observations are particularly useful for studying
low cloudiness because human observers identify clouds by morphological type,
which is qualitatively related to the dynamical and thermodynamical environment
in which the clouds occur. Thus, global
climatologies of low cloud types can be used to qualitatively infer typical
boundary layer structures around the world where above-surface measurements are
lacking. This will help elucidate processes responsible for variability in
cloudiness and can also provide a baseline for the development and validation of
boundary layer cloud parameterizations in general circulation models.
For more information see:
